Modeling operation of a tool in a wellbore

ABSTRACT

In modeling operation of a well tool in applying a force to a device in a well, a computing system receives inputs representing a plurality of geometric characteristics of the well tool. The computing system also receives inputs representing a plurality of geometric characteristics of the well. The computing system determines based on the geometric characteristics of the well tool and the well, a predicted reaction force on a portion of the well tool that affects operation of the well tool in applying force to the device. The predicted reaction force is due to contact between a surface associated with the well tool and a surface of the well.

TECHNICAL BACKGROUND

This disclosure relates to modeling operation of a tool string in awell.

BACKGROUND

Although wells are formed with dimensions to allow passage of tools fromthe surface, the inexact nature of wellbore formation and completionmay, in fact, block or hinder passage of tools. Predicting whetherand/or how particular geometries of well tools and the well tool string(e.g., diameters of particular components, lengths of particularcomponents, and otherwise) may interact with the well (or other downholetubular structure, such as a casing or liner) while under real-worldoperational conditions may allow well site operators to make decisionsregarding, for instance, tool string component selection andarrangement, well design, and other factors (e.g., feasibility of cablemechanics, drum crush potential, tractoring requirements) in drillingand production operations.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic of an example well in side cross-section, a welltool string, and a distributed computing system that includes a modelingsystem operable to model interactions between the well tool string andthe well;

FIG. 2 is a functional diagram of a modeling system configured tooperate on a computing system;

FIGS. 3A-3B illustrate example graphical user interfaces of a 3Dmodeling system;

FIGS. 4A-4B illustrate example graphical outputs of a 3D modelingsystem;

FIG. 5 illustrates an example well tool string that have particulargeometrical characteristics;

FIGS. 6A-6B are flowcharts describing example methods for modelingpassage of a well string through a portion of a well;

FIGS. 7A-7B are flowcharts describing example methods for modelingoperation of a well tool in applying a force to a device in a well; and

FIGS. 8A-8B illustrate flowcharts describing example methods formodeling passage of a well tool through a portion of a well using anadaptive machine learning model.

DETAILED DESCRIPTION

FIG. 1 illustrates an example embodiment of a well 100 with a well toolstring 125 passing through the well 100. Components of the well 100 andthe tool string 125 are communicably coupled with a distributedcomputing system 150 that includes a modeling system 185 operable tomodel interactions between the well tool string 125 and the well 100,for example, passage of the tool string 125 through the well 100.

The illustrated well 100 includes the wellbore 110 extending to and/orthrough one or more subterranean zones, such as the illustratedsubterranean zone 130, from a terranean surface 105. Althoughillustrated as extending from the terranean surface 105, the wellbore110 may be formed from the Earth's surface at a location under a body ofwater rather than the terranean surface 105. In other words, in someembodiments, the terranean surface 105 may be an ocean, gulf, sea, orany other body of water. In short, reference to the terranean surface105 includes both land and water surfaces and contemplates formingand/or developing one or more wellbores from either or both locations.

One or more strings of casing may be set in place in the wellbore 110.For example, the illustrated well system 100 includes a conductor casing115, which extends from the terranean surface 105 shortly into theEarth. A portion of the wellbore 110 enclosed by the conductor casing115 may be a large diameter borehole. Downhole of the conductor casing115 may be additional lengths of casing 120. The casing 120 may enclosea slightly smaller borehole and protect the wellbore 110 from intrusionof, for example, freshwater aquifers located near the terranean surface105, and/or isolate hydrocarbon production from specific layers.

The illustrated wellbore 110 includes a substantially vertical portionand a directional portion. The vertical portion of the wellbore 110 mayextend generally vertically downward toward a kickoff point and thenturn at an angle towards the directional (e.g., radiussed, slant,horizontal) portion of the wellbore 110.

Although illustrated as a substantially vertical wellbore with adirectional portion extending from the vertical portion, the presentdisclosure contemplates that vertical, directional, horizontal, slant,articulated, radiussed, and other types of wellbores may be includedand/or formed within the well system 100.

Although at least a portion of the wellbore 110 is illustrated as asubstantially vertical wellbore, even vertical wellbores (cased or openhole) may include slight turns (e.g., corkscrewing), crevices,shoulders, divots, and other inconsistencies in the formation of thewellbores. Such inconsistencies may be contact, catch, or hang-uppoints/surfaces that a well tool string, such as the well tool string125 may come into contact with, interfere with, and hang-up on duringtips into and out of the wellbore 110.

Continuing with FIG. 1, as illustrated, the wellbore 110 extends intoand through a subterranean formation 130. The illustrated subterraneanformation 130 is a hydrocarbon bearing formation, such as, for example,shale, sandstone, coal, or other geologic formation that contains oil,gas, or other hydrocarbons, but could be other types of formations. Oncethe wellbore 110 is formed, the basic physics underlying productioninvolves a migration of fluids (liquids and/or gas) through permeablerock formations such as the subterranean formation 130 to areas of lowerpressure created by the wellbore 110. These fluids may then flow througha casing of the wellbore 110 or an open hole completion and areeventually brought to the surface.

Extending above the terranean surface 105, as illustrated, is a wellhead145. The wellhead 145 can contain or be coupled to a broad array ofcomponents, including sensors (e.g., temperature, pressure, flow andother sensors), valves, blow-out-preventers, snubbing heads, and othercomponents. The wellhead 145 may support a tubular, such as a productiontubing string 140 extending through the annulus 112 of the wellbore 110.In some embodiments, the tubing string 140 may receive the well toolstring 125 as it is run into the well 100. Here, the well tool string125 is depicted as being conveyed on a line 135, but in other instancesthe tool string 125 may incorporate additional tubing, including jointedor coiled tubing, for conveying the tools thereof into and out of thewell 100. The line 135 may be any type line for conveying the toolstring 125 into and out of the well 100, including wireline, slickline,electric (e-line), and other. In the present example, the line 135 is anelectric line (e-line) that facilitates a supply of electric power,control, and data between the terranean surface 105 (e.g., fromcomputing system 150 or other control system or controller) and the welltool string 125. Typically, the electric line 135 may be connected by adrum and spooled off of a wireline truck to a wireline sheave (notshown). In certain instances, conveyance of the tool string 125 throughthe well can be assisted, for example, by well tractor, autonomous wellrobot, by being pumped, and or in another manner.

The electric line 135 is coupled to the well tool string 125, whichcomprises well tools 126, 127, and 128. Although three well tools areillustrated as part of the well tool string 125, there may be more, orfewer, well tools as part of the well tool string 125 depending on, forinstance, the type of operation performed by the well tool string 125.Some example well tools include, for example, sensors (e.g.,temperature, pressure, MWD, LWD, and others), rope sockets,accelerators, detent jars, stems, spang jars, and/or other tools.

The sensors in the well tool string 125 and/or wellhead 145 and anyother sensors of the well 100 are coupled to the computing system 150through one or more communication links 147. Generally, thecommunication links 147 may be any wired or wireless communicationprotocol and equipment operable to transfer data (e.g., measuredinformation, instructions, and other data), either in real-time (e.g.,without intentional delay, given the processing limitations of thesystem and the time required to accurately measure data), near real-time(e.g., at or near real-time and accounting for some processing time butwith no human-appreciable delays that are due to computer processinglimitations), or at a delayed time (e.g., accounting for human userinteraction). For example, in some embodiments, the communication links147 may facilitate transfer of data between the computing system 150 (orother computing system or controller communicably coupled to thecomputing system 150) and the well tool string 125, wellhead 145 and/orother sensors of the well 100 during the operations (e.g., MWD, LWD, orslickline operations). Alternatively, data may be transferred before orafter completion of such operations, such as, for example, after thewell tool string 125 has been removed to the terranean surface 105. Inany event, the present disclosure contemplates that data is transferredwithin an appropriate time frame commensurate with the operations beingperformed with well system 100.

The illustrated computing system 150 includes a number of clients 155, aserver system 165, and a repository 190 communicably coupled through anetwork 160 by one or more communication links 147 (e.g., wireless,wired, or a combination thereof). The computing system 150, generally,executes applications and analyzes data during, before, and after one ormore operations (e.g., drilling, completion, workover, and otherwise)performed by well system 100. For instance, the computing system 150 mayexecute the modeling system 185 to model passage of the well tool string125 through the wellbore 110 and other tubulars, such as the tubing 140.

In general, the server system 165 is any server that stores one or morehosted applications, such as, for example, the modeling system 185. Insome instance, the modeling system 185 may be executed via requests andresponses sent to users or clients within and communicably coupled tothe illustrated computing system 150 of FIG. 1. In some instances, theserver system 165 may store a plurality of various hosted applications,while in other instances, the server system 165 may be a dedicatedserver meant to store and execute only a single hosted application, suchas the modeling system 185.

In some instances, the server system 165 may comprise a web server,where the hosted applications represent one or more web-basedapplications accessed and executed via network 160 by the clients 155 ofthe system to perform the programmed tasks or operations of the hostedapplication. At a high level, the server system 165 comprises anelectronic computing device operable to receive, transmit, process,store, or manage data and information associated with the computingsystem 150. Specifically, the server system 165 illustrated in FIG. 1 isresponsible for receiving application requests from one or more clientapplications associated with the clients 155 of computing system 150 andresponding to the received requests by processing said requests in theassociated hosted application, and sending the appropriate response fromthe hosted application back to the requesting client application.

In addition to requests from the external clients 155 illustrated inFIG. 1, requests associated with the hosted applications may also besent from internal users, external or third-party customers, otherautomated applications, as well as any other appropriate entities,individuals, systems, or computers. As used in the present disclosure,the term “computer” is intended to encompass any suitable processingdevice. For example, although FIG. 1 illustrates a single server system165, computing system 150 can be implemented using two or more serversystems 165, as well as computers other than servers, including a serverpool. Indeed, server system 165 may be any computer or processing devicesuch as, for example, a blade server, general-purpose personal computer(PC), Macintosh, workstation, UNIX-based workstation, or any othersuitable device. In other words, the present disclosure contemplatescomputers other than general purpose computers, as well as computerswithout conventional operating systems. Further, illustrated serversystem 165 may be adapted to execute any operating system, includingLinux, UNIX, Windows, Mac OS, or any other suitable operating system.

In the illustrated embodiment, and as shown in FIG. 1, the server system165 includes a processor 170, an interface 180, a memory 175, and themodeling system 185. The interface 180 is used by the server system 165for communicating with other systems in a client-server or otherdistributed environment (including within computing system 150)connected to the network 160 (e.g., clients 155, as well as othersystems communicably coupled to the network 160). Generally, theinterface 180 comprises logic encoded in software and/or hardware in asuitable combination and operable to communicate with the network 160.More specifically, the interface 180 may comprise software supportingone or more communication protocols associated with communications suchthat the network 160 or interface's hardware is operable to communicatephysical signals within and outside of the illustrated computing system150.

Generally, the network 160 facilitates wireless or wirelinecommunications through the communication links 147 between thecomponents of the computing system 150 (e.g., between the server system165 and the clients 155), as well as with any other local or remotecomputer, such as additional clients, servers, or other devicescommunicably coupled to network 160 but not illustrated in FIG. 1. Thenetwork 160 is illustrated as a single network in FIG. 1, but may be acontinuous or discontinuous network without departing from the scope ofthis disclosure, so long as at least a portion of the network 160 mayfacilitate communications between senders and recipients. The network160 may be all or a portion of an enterprise or secured network, whilein another instance at least a portion of the network 160 may representa connection to the Internet. In some instances, a portion of thenetwork 160 may be a virtual private network (VPN), such as, forexample, the connection between the clients 155 and the server system165. Further, all or a portion of the network 160 can comprise either awireline or wireless link. Example wireless links may include802.11a/b/g/n, 802.20, WiMax, and/or any other appropriate wirelesslink. In other words, the network 160 encompasses any internal orexternal network, networks, sub-network, or combination thereof operableto facilitate communications between various computing components insideand outside the illustrated computing system 150. The network 160 maycommunicate, for example, Internet Protocol (IP) packets, Frame Relayframes, Asynchronous Transfer Mode (ATM) cells, voice, video, data, andother suitable information between network addresses. The network 160may also include one or more local area networks (LANs), radio accessnetworks (RANs), metropolitan area networks (MANs), wide area networks(WANs), all or a portion of the Internet, and/or any other communicationsystem or systems at one or more locations.

As illustrated in FIG. 1, server system 165 includes a processor 170.Although illustrated as a single processor 170 in FIG. 1, two or moreprocessors may be used according to particular needs, desires, orparticular embodiments of computing system 150. Each processor 170 maybe a central processing unit (CPU), a blade, an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), oranother suitable component. Generally, the processor 170 executesinstructions and manipulates data to perform the operations of serversystem 165 and, specifically, the modeling system 185. Specifically, theserver's processor 170 executes the functionality required to receiveand respond to requests from the clients 155 and their respective clientapplications, as well as the functionality required to perform the otheroperations of the modeling system 185.

Regardless of the particular implementation, “software” may includecomputer-readable instructions, firmware, wired or programmed hardware,or any combination thereof on a tangible medium operable when executedto perform at least the processes and operations described herein.Indeed, each software component may be fully or partially written ordescribed in any appropriate computer language including C, C++, C#,Java, Visual Basic, assembler, Perl, any suitable version of 4GL, aswell as others. It will be understood that while portions of thesoftware illustrated in FIG. 1 are shown as individual modules thatimplement the various features and functionality through variousobjects, methods, or other processes, the software may instead include anumber of sub-modules, third party services, components, libraries, andsuch, as appropriate. Conversely, the features and functionality ofvarious components can be combined into single components asappropriate. In the illustrated computing system 150, processor 170executes one or more hosted applications on the server system 165.

At a high level, the modeling system 185 is any application, program,module, process, or other software that may execute, change, delete,generate, or otherwise manage information according to the presentdisclosure, particularly in response to and in connection with one ormore requests received from the illustrated clients 155 and theirassociated client applications. In certain cases, only one modelingsystem 185 may be located at a particular server system 165. In others,a plurality of related and/or unrelated modeling systems may be storedat a single server system 165, or located across a plurality of otherserver systems 165, as well. In certain cases, computing system 150 mayimplement a composite hosted application. For example, portions of thecomposite application may be implemented as Enterprise Java Beans (EJBs)or design-time components may have the ability to generate run-timeimplementations into different platforms, such as J2EE (Java 2 Platform,Enterprise Edition), ABAP (Advanced Business Application Programming)objects, or Microsoft's .NET, among others. Additionally, the hostedapplications may represent web-based applications accessed and executedby remote clients 155 or client applications via the network 160 (e.g.,through the Internet).

Further, while illustrated as internal to server system 165, one or moreprocesses associated with modeling system 185 may be stored, referenced,or executed remotely. For example, a portion of the modeling system 185may be a web service associated with the application that is remotelycalled, while another portion of the modeling system 185 may be aninterface object or agent bundled for processing at a remote clients155. Moreover, any or all of the modeling system 185 may be a child orsub-module of another software module or enterprise application (notillustrated) without departing from the scope of this disclosure. Stillfurther, portions of the modeling system 185 may be executed by a userworking directly at server system 165, as well as remotely at clients155.

The server system 165 also includes memory 175. Memory 175 may includeany memory or database module and may take the form of volatile ornon-volatile memory including, without limitation, magnetic media,optical media, random access memory (RAM), read-only memory (ROM),removable media, or any other suitable local or remote memory component.Memory 175 may store various objects or data, including classes,frameworks, applications, backup data, business objects, jobs, webpages, web page templates, database tables, repositories storingbusiness and/or dynamic information, and any other appropriateinformation including any parameters, variables, algorithms,instructions, rules, constraints, or references thereto associated withthe purposes of the server system 165 and its one or more hostedapplications. Additionally, memory 175 may include any other appropriatedata, such as VPN applications, firmware logs and policies, firewallpolicies, a security or access log, print or other reporting files, aswell as others.

The illustrated computing system 150 of FIG. 1 also includes one or moreclients 155. Each client 155 may be any computing device operable toconnect to or communicate with at least the server system 165 and/or viathe network 160 using a wireline or wireless connection. Generally, eachclient 155 includes a processor, an interface, a memory, as thosecomponents are described above, as well as a client application and agraphical user interface (GUI) 157. In general, each client 155comprises an electronic computer device operable to receive, transmit,process, and store any appropriate data associated with the computingsystem 150 of FIG. 1. It will be understood that there may be any numberof clients 155 associated with, or external to, computing system 150.For example, while illustrated computing system 150 includes two clients155, alternative implementations of computing system 150 may include asingle client 155 communicably coupled to the server system 165, or anyother number suitable to the purposes of the computing system 150.Additionally, there may also be one or more additional clients 155external to the illustrated portion of computing system 150 that arecapable of interacting with the computing system 150 via the network160. Further, the term “client” and “user” may be used interchangeablyas appropriate without departing from the scope of this disclosure.Moreover, while each client 155 is described in terms of being used by asingle user, this disclosure contemplates that many users may use onecomputer, or that one user may use multiple computers.

As used in this disclosure, client 155 is intended to encompass apersonal computer, touch screen terminal, workstation, network computer,kiosk, wireless data port, smart phone, personal data assistant (PDA),one or more processors within these or other devices, or any othersuitable processing device. For example, each client 155 may comprise acomputer that includes an input device, such as a keypad, touch screen,mouse, or other device that can accept user information, and an outputdevice that conveys information associated with the operation of theserver system 165 (and the modeling system 185) or the client 155itself, including digital data, visual information, the clientapplication, or the GUI 157. Both the input and output device mayinclude fixed or removable storage media such as a magnetic storagemedia, CD-ROM, or other suitable media to both receive input from andprovide output to users of the clients 155 through the display, namely,the GUI 157.

In some instances, a particular client 155 is specifically associatedwith an administrator of the illustrated computing system 150. Theadministrator can modify various settings associated with one or more ofthe other clients 155, the server system 165, the modeling system 185,and/or any relevant portion of computing system 150. For example, theadministrator may be able to modify the relevant default valuesassociated with the modeling system 185.

Each of the illustrated clients 155 includes a GUI 157 comprising agraphical user interface operable to interface with at least a portionof computing system 150 for any suitable purpose, including generating avisual representation of the client application (in some instances, theclient's web browser) and the interactions with the hosted application,including the responses received from the hosted application received inresponse to the requests sent by the client application. Generally,through the GUI 157, the user is provided with an efficient anduser-friendly presentation of data provided by or communicated withinthe system. The term “graphical user interface,” or GUI, may be used inthe singular or the plural to describe one or more graphical userinterfaces and each of the displays of a particular graphical userinterface. Therefore, the GUI 157 can represent any graphical userinterface, including but not limited to, a web browser, touch screen, orcommand line interface (CLI) that processes information in computingsystem 150 and efficiently presents the information results to the user.

In general, the GUI 157 may include a plurality of user interface (UI)elements, some or all associated with the client application and/or themodeling system 185, such as interactive fields, pull-down lists, andbuttons operable by the user at clients 155. These and other UI elementsmay be related to or represent the functions of the client application,as well as other software applications executing at the clients 155. Inparticular, the GUI 157 may be used to present the client-basedperspective of the modeling system 185, and may be used (as a webbrowser or using the client application as a web browser) to view andnavigate the hosted application, as well as various web pages locatedboth internal and external to the server, some of which may beassociated with the hosted application.

The illustrated repository 190 may be any database or data storeoperable to store data 195 associated with the well system 100.Generally, the data 195 may comprise inputs to the modeling system 185,historical information of the well system 100 or other well systems, andoutput data from the modeling system 185. For instance, the data 195 mayinclude inputs 240, 245, 250, as well as data from a solid model store225, a tool/well/fluids specifications store 230, or a history store 235as shown and described with reference to FIG. 2. The data 195 may alsoinclude outputs 255, 260, and/or 270 shown and described with referenceto FIG. 2.

FIG. 2 depicts an example tool passage modeling system 200 that can beused as modeling system 185. The modeling system 200 has a number ofdifferent modules—a 3D geometric model 215, a mathematical model 220 andan adaptive machine learning model 210. Each of the modules will bedescribed in more detail below. The modules can be operated separatelyor operated together, in series and/or parallel or otherwise, to provideinformation about passage through and/or operation of well tools in thewell. For example, in certain instances, two or more of the modules canbe operated in evaluating the same scenario to provide a greater degreeof confidence, and presumably accuracy, than operation of a singlemodule. In another example, two modules can be operated in evaluatingthe same scenario, and the third operated as a tie breaker only if theoutcomes predicted by the first two modules operated conflict.

In certain instances, the modeling system 185 can be operated in thewell design phase to evaluate multiple possible well configurations inconnection with multiple possible tool string configurations andconveyances (e.g., line, coiled tubing, or jointed tubing and whether atractor will be used, whether conveyance will be assisted by pumpingfluid, and other). In certain instances, the modeling system 185 can beoperated using conditions of an existing well in the tool string designphase to iteratively evaluate multiple possible tool stingconfigurations and conveyances. From the multiple possible tool stringconfigurations and conveyances and multiple possible wellconfigurations, if done during the well design phase, the operator canselect the combination that will allow the tool string to pass the wellto/from a specified depth, that requires the least or below a specifiedamount of force to pass to/from the specified depth, and/or that willprovide the greatest or above a specified amount of force in operating,for example, in a jarring operation. In instances where the modelingsystem 185 is operated concurrently with the tool string being passedthrough the interval of the well, the operator may use information fromthe modeling system 185 to test and select well and/or tool stringconditions, such as fluid flow rates, pressures, orientation of the toolstring and/or other conditions, and then adjust the conditionsdynamically—as the tubing string is being passed through the interval ofthe well—to improve, over the current conditions, the likelihood of thetool sting passing through the interval, decrease the forces necessaryto pass the tool string through the interval, and/or improve theeffectiveness of the tool string's operation, for example, in jarringoperations.

The mathematical model 220 uses a simplified approximation of the toolstring geometry and a simplified approximation of well geometry todetermine whether the tool string can pass through an interval of thewell. In certain instances, the mathematical model 220 can furtherdetermine the forces acting on the tool string, and tools thereof, atvarious locations along the interval, and how much force is needed to beapplied to the tool string to pass the tool string downhole through thewell interval and/or uphole through the interval. In certain instances,the mathematical model 220 can use the force information in evaluatingoperation of the tool string and tools thereof. The interval can be aportion of the well or the entire well. For example, in certaininstances, the interval spans from the top of the well, at a wellhead,to some specified depth to which it is expected that the tool stringwill be run. Some example models that can be used in providing thefeatures of mathematical model 220 include InSite For Well Intervention,where InSite is a registered trademark of Halliburton Energy Services,Inc., and Cerberus, a registered trademark of National Oil Well Varcoand the underlying calculations of which are described in more detail inBasic Tubing Forces Model (TFM) Calculation, Tech Note CTES, L.P., 2003.These example models can be classified as 2D in nature.

The mathematical model 220 receives a number of inputs 245 from which itdetermines whether the tool string can pass the interval and the forcesinvolved. The inputs 245 can be user input and/or can come from othersystems communicably coupled to the mathematical model 220. The inputs245 include tubing string characteristics, well characteristics, fluidcharacteristics and other characteristics. In certain instances, themathematical model 220 can be operated concurrently with the real-worldoperations it is modeling and provide information on passage of the toolstring and the forces involved at approximately the same time,accounting for time taken to perform the computing, that the modeledtool passage and/or forces are happening in the real-world. For example,the model 220 may be operated to model the real-world in real time.Certain of the inputs 245, for example those that may vary with time,can be provided to the mathematical model 220 without substantial orintentional delay, for example, in real time.

The tubing string characteristics include information about the toolsand other components of the tool string. Some examples can includegeometric characteristics such as the maximum outside and insidediameters of the tools and other components of the tool string, thelengths of the tools and other components of the tool string, the typesof tools and other components arranged in the tool string, what orderthe tools and other components are arranged in the tool string, the typeof connection between the tools and other components of the tool string,tool and component weights, whether the tool string is comprisedentirely of tubing and tools or whether the string is of the typedeployed on line (e.g., wireline, slickline, e-line or other) and, if online, the characteristics of the line such as the weight and diameter ofthe line, and other information. The tool string characteristics canalso include material properties such as the types of material of thetools and components of the tool string and line (if provided), theyield and plastic strength and elastic modulus of the materials, thefrictional characteristics of the materials and other information. Thetool string characteristics can also include dynamic properties such asapplied torque and forces, rotational and axial movement speeds andother information.

The tool string characteristics can also include particular informationabout the tools that make up the tool string. For example, the toolstring characteristics can include information about whether the toolhas rollers and how and where they reduce the frictional coefficients ofthe system, centralizers, knuckle joints and how and where they reducethe stiffness of the tool string, packers, nozzles or other flowrestrictions, and/or other tool string characteristics. In certaininstances, the tubing string characteristics can include additional ordifferent information than geometric, material, dynamic and toolspecific characteristics. In FIG. 2, the mathematical model 220 iscoupled to a data store 230 that includes a database of tools or othercomponents that could be used in the tool string, for example identifiedby manufacturer, model number, size and pressure rating, correlated totheir characteristics. The data store 230 may be coupled to a GUI thatallows the operator to select the tools and other components of the toolstring being analyzed from a list and/or by manually inputting theidentifier. Thereafter, the tools string characteristics are populatedto the mathematical model 220 from the data store 230 based on theuser's input. FIG. 3A shows a GUI 300 for facilitating an operator'saccess to the information in data store 230, and includes pull downmenus with lists of particular tubing string components and produces acomputer-generated real-world looking image 305 of the tool string andwell.

The well characteristics include information about the wellbore and thecomponents, such as casing and completion string components, installedin the wellbore that make up the well. Some examples can includegeometric characteristics of the wellbore, such as the diameter of thewellbore at different positions along the length of the well, thetrajectory of the wellbore at different positions along the length ofthe well, the eccentricity of the wellbore at different positions alongthe length of the well, surface roughness in open hole portions of thewell, and other information. In certain instances, the geometriccharacteristics of the wellbore can be obtained from survey data, suchas survey logs (having information on inclination relative to gravityand direction per depth), caliper logs (diameter per depth) and otherdata, and imported into the mathematical model 220. The geometriccharacteristics of the components installed in the wellbore can includethe outside and inside diameters of the components at differentpositions along the length of the well, the lengths of the components,the types of components, their order in the well, the type of connectionor other interface between the components, flow restrictions through thecomponents, component weights, and other information. The wellcharacteristics can also include material properties such as the typesof material of the well components, the yield and plastic strength andelastic modulus of the materials, the frictional characteristics of thematerials and other information. In certain instances, the wellcharacteristics can include additional information beyond geometric andmaterial characteristics.

In FIG. 2, the data store 230 can also be used and populated with adatabase of well components, for example identified by manufacturer,model number, size and pressure rating, correlated to theircharacteristics. As above, the data store 230 may be coupled to a GUIthat allows the operator to select the components being analyzed from alist and/or by manually inputting the identifier. Thereafter, thecomponent characteristics are populated to the mathematical model 220from the data store 230 based on the user's input. The GUI can besimilar to the GUI 300 of FIG. 3A.

The fluid characteristics include information about the fluids in thewell and the tool string. Some examples can include staticcharacteristics such as fluid type (e.g., gas, liquid, mixture), fluidviscosity, fluid density, pressures inside and out of the tool stringand within the well at the surface and (if available) downhole,temperatures at different portions along the length of the well andother information. The fluid characteristics can also include dynamiccharacteristics such as flow rate, pressures inside and out tool stringand within well, and other information. Some or all of this informationcould be provided at approximately the same time as it is occurring inthe real-world, for example, in real time. Additional or differentinformation could be provided. In FIG. 2, the data store 230 can also beused and populated with a database of fluid characteristics. As above,the data storage and 30 may be coupled to a GUI that allows the operatorto select the fluids in a well from a list and/or by manually inputtingand identifier. Thereafter, the characteristics of fluid are populatedto the mathematical model 220 from the data store 230 based on user'sinput. FIG. 3B shows another view of the GUI 300 for facilitating anoperator's access to the information in data store 230, and includespull down menus with lists of particular fluids and fluidcharacteristics.

The other characteristics can include other information that themathematical model 220 can account for. Some examples of othercharacteristics include how much force can be applied to the tool stringfrom the surface (e.g., by the rig) and/or equipment in the well (e.g.,by a well tractor), safety factors, and other characteristics. Incertain instances, the other characteristics can include the real-worldtool string position and orientation information and other informationat approximately the same time as it is occurring in the real-world, forexample, in real time. Additional or different information could beprovided.

Some example inputs 245 include tool length, tool OD profile, toolweight, cable diameter, cable stretch coefficient, cable breakingstrength, cable weight in air, cable weight in water, cable drum crushcaution, cable drum crush warning, cablehead weak point design,allowable % breaking strength, borehole diameter profile, borehole fluiddensity, borehole fluid viscosity, borehole temperature profile,borehole coefficient of friction profile, well trajectory profile (MD,INC, AZI), borehole roughness, trajectory eccentricity, tool stresslimitations, run in hole/pull out of hole (RIH/POOH) running speed,surface pressure, wellhead friction, flowrate for gas and liquids, 2D IDprofile, and/or others.

The mathematical model 220 simulates passage of the tool string throughthe interval of the well, performing calculations based on the inputcharacteristics to determine whether the tool string, under thespecified conditions input into the model (including the available forceto drive the tool string), will pass axially, uphole and/or downhole,through the interval of the well without exceeding specified stresslimits of the tool string or its associated line, if line deployed. Inaddition, the mathematical model 220 can determine the forces involvedin moving the tool string axially in the interval of the well, includingthe axial forces required to move the tool string and the reactionforces between the tool string and well. In evaluating operation of thetool string and tools thereof, the input characteristics areadditionally used to determine local reaction forces that would reducethe effectiveness of tools that move in the well when operating.

For simplicity, the mathematical model 220 assumes the tools of the toolstring are uniform diameter, and does not take into account the shape ofthe outwardly facing, lateral surfaces of the tool string. Similarly,the mathematical model 220 assumes that the surfaces of the welldirectly adjacent the tool string are uniform diameter, and does nottake into account the shape of the inwardly facing, lateral surfaces ofthe well. Thus, in general terms, the calculations determine whether along cylindrical solid body (i.e., the tool string) can pass through along cylindrical tube (i.e., the well) and the forces involved. Themathematical model 220 accounts for the tube's changes in trajectory(e.g., bends, cork-screwing, and the like) and the resulting reactionforces and frictional forces between the cylindrical body and thecylindrical tube as the cylindrical body must bend to traverse thechanges in trajectory, otherwise deforms under loads, and expands andcontracts due to temperature and pressure. The mathematical model 220additional accounts for external forces acting on the cylindrical body,such as fluidic forces, push/pull on the tool string and/or the line,and gravity.

The mathematical model 220 provides the calculated information above inoutputs 260. The outputs 260 include the values of such information inthe form of single outputs, tables and graphs. In certain instances, theoutputs 260 include values of the calculated information correlated tothe locations in the interval to which they relate, for example, forceversus depth tables or graphs, deformation versus depth tables orgraphs, and other. The information can yield a surface tubing/cableforce profile indicating the forces needed to be applied to thetubing/cable at the surface rig to push and/or pull the tool stringthrough specified locations in the interval of the wellbore, given thefrictional and other forces acting on along the length of the toolstring and/or line resisting the surface force. Correspondingly, theinformation can yield the force realized, in pull and/or push, in thetool string or line supporting the tool string at specified locationsalong the length of the well, including at the line-to-tool stringconnection, given the force input at the surface and the frictional andother forces acting along the length of the tool string and/or lineresisting that force. The mathematical model 220 can also determine theaxial deformation of the tool string and/or line supporting the toolstring at specified locations in the interval due to the applied forces.

Some example outputs 260 include surface cable tension profile, downholecablehead tension profile, maximum flow/injection rate profile, maximumoverpull profile, cable stretch profile, well contact force profile,axial effective force profile, and/or others.

In evaluating operation of the tool string and tools thereof, themathematical model 220 can determine the forces involved in moving aportion of a tool in the tool string at a specified location in thewell. For example, in the context of the tool having a portion that isor can potentially be in contact with a surface of the well and thatmoves relative to the well, the reaction forces between the movingportion of the tool and the well can affect the force available to acton and move the moving portion. Additionally, the reaction forcesbetween other portions of the tool string and the surfaces of the wellcan affect the force available to act on and move the moving portion ofthe tool whether or not the moving portion of the tool is or canpotentially be in contact with a surface of the well. The moving portionof the tool may also be coupled to an actuator that provides force tomove the moving portion (e.g., hydraulic, electric, pneumatic, springand/or other type of actuator). Other components in the tool string andtheir arrangement in the tool string, for example, due to their weight,damping/stiffness characteristics, dynamic characteristics including ifand how they are moved relative to the moving portion (e.g., dropped,pushed or pulled) and other characteristics, may affect the forceavailable to act on and move the moving portion. Finally, externalforces acting on the tool string and/or tool can affect the forceavailable to act on and move the moving portion. In evaluating operationof the tool string, the mathematical model 220 can account for each ofthese factors and calculate the net force required and/or available tomove the moving portion. For example, in the context of a jarring toolor a setting tool, the mathematical model 220 can determine the jarringforce or the setting force (uphole and/or downhole) the tool can providewhen operated at a specified location in the interval of the well. Theinformation can be output as outputs 260.

Finally, in certain instances, the mathematical model 220 can beoperated concurrently with the real-world operations it is modeling toperform some or all of the analysis described above and provide theoutputs 260 at approximately the same time, accounting for time taken toperform the computing, that the information output is occurring in thereal-world.

The 3D geometric model 215 performs a similar analysis to themathematical model 220, however, using a more complex approximation ofthe tool string geometry and the well geometry to determine whether thetool string can pass through an interval of the well. Specifically, the3D geometric model 215 takes into account surface shape features of thetool string and well by using information from 3D solid models. Theinformation includes not only information about the shape of the endfacing surfaces of the tool string and the well, but also informationabout the shape of the outwardly facing, lateral surfaces of the toolstring and inward facing, lateral surfaces of the well directly adjacentthat contact or potentially contact the tool string. As a result, the 3Dgeometric model 215 can predict interactions in the geometries of thetool string and the well that an approximation assuming a constantdiameter, such as in the mathematical model 220, cannot. With this 3Dinformation the 3D geometric model 215, thus, can determine whether thetool string can pass through an interval of the well, the forces actingon the tool string, and tools thereof, at various locations along theinterval, and how much force is needed to be applied to the tool stringto pass the tool string downhole through the well interval and/or upholethrough the interval. In certain instances, like the mathematical model220, the 3D geometric model 215 can use the force information inevaluating operation of the tool string and tools thereof.

The 3D geometric model 215 can receive the same or similar inputs 240 asthe inputs 245 into mathematical model 220, including tool stringcharacteristics, the well string characteristics, the fluidcharacteristics, and other characteristics. Additionally, the 3Dgeometric model 215 receives three-dimensional data about the toolstring and the well. In certain instances, the 3D geometric model 215can be operated concurrently with the real-world operations it ismodeling and provide information on passage of the tool string and theforces involved at approximately the same time, accounting for timetaken to perform the computing, that the modeled tool passage forces arehappening in the real-world. For example, the 3D geometric model 215 maybe operated to model the real-world in real time. Certain of the inputs245 can be provided to the 3D geometric model 215 without substantial orintentional delay, for example, in real time.

Some example inputs 245 into the 3D geometric model 215 include surfacecable tension profile, downhole cablehead tension profile, well contactforce profile, axial effective force profile, tool length, tool ODprofile, tool weight, borehole diameter profile, borehole fluid density,borehole fluid viscosity, borehole temperature profile, boreholecoefficient of friction profile, well trajectory profile (MD, INC, AZI),borehole roughness, trajectory eccentric, tool stress limitations, runin hole/pull out of hole (RIH/POOH) running speed, tool flex joints,tool knuckle joints, tool standoff profile, tool decentralizer profile,tool centralizer profile, tool mobility—roler boogie profile, formationcompaction projected dynamic profile, thermal induced mechanicaldistortion profile, 2D ID profile and/or others.

In FIG. 2, the 3D geometric model 215 is coupled to a data store 225that includes a database of three-dimensional data, for exampleextracted from solid models and/or physical measurements, of the toolsand other components of the tool string and the well components, forexample, identified by manufacturer, model number, size and pressurerating. As above, the data store 225 may be coupled to a GUI that allowsthe operator to select components being analyzed from a list and/orentered manually. Thereafter, the tool/component characteristics arepopulated to the geometric model 215 from the data store 225 based onthe user's input.

In certain instances, the 3D geometric model 215 can be configured tooperate in concert with the mathematical model 220, such that someinputs 240 to the geometric model 215 are outputs 260 from themathematical model 220. Therefore, in addition to the tool stringcharacteristics, well string characteristics, fluid characteristics, andother characteristics described above, the 3D geometric model 215 cantake as inputs 240 information on the axial force required to pull orpush the tool string through a specified location in the interval of thewell and the corresponding contact reaction forces imposed on the toolstring provided as outputs 260 from the mathematical model 220. Incertain instances, this information can include the surface tubing/cableforce profile in the force at the line to tool string connection. Ininstances where the 3D geometric model 215 is configured to operate as astand-alone model, it need not receive information from the mathematicalmodel 220.

Like the mathematical model 220, the 3D geometric model 215 simulatespassage of the tool string through the interval of the well, performingcalculations based on the input characteristics to determine whether thetool string, under the specified conditions input into the model(including the available force to drive the tool string), will passaxially, uphole and/or downhole, through the interval of the wellwithout exceeding specified stress limits of the tool string or itsassociate line, if line deployed. In addition, in certain instances, the3D geometric model 215 can determine the forces involved in moving thetool string axially in the interval of the well, including the axialforces required to move the tool string and the reaction forces betweenthe tool string and well. The calculations performed by the 3D geometricmodel 215, in certain instances, can be similar to those described abovewith respect to the mathematical model 220 and/or in Basic Tubing ForcesModel (TFM) Calculation, Tech Note CTES, L.P., 2003, mentioned above.However, unlike the mathematical model 220, the analysis performed bythe 3D geometric model 215 additionally models and accounts for the 3Dshape of the end facing and lateral surfaces of the tool string and thewell and how they interact. This analysis is over and above the effectsthat surface finish, for example, would have on friction, and takes intoaccount how recessed or upstanding features of one can engage recessedor upstanding features of the other and how the shapes of those featureswill interact to resist or lock the tool string against movement. Thus,in general terms, the 3D geometric model 215 determines whether thetubing string, including the 3D features of its end facing and lateralsurfaces, can pass through the well, including the 3D features of itsend facing and lateral surfaces, and the forces involved. The 3Dgeometric model 215 accounts for the well's changes in trajectory (e.g.,bends, cork-screwing, and the like) and the resulting reaction forcesand frictional forces between the tubing string (including the 3Dfeatures) and the well (including the 3D features) as the tubing stringmust bend to traverse the changes in trajectory, otherwise deforms underloads, and expands and contracts due to temperature and pressure. Thegeometric model 215 additionally accounts for external forces acting onthe tubing string, such as fluidic forces, push/pull on the tool stringand/or the line, and gravity. This modeling can be considered 3D andevaluates relative positions of the tool string with respect to thewellbore it is traveling through, and is not restricted to evaluating totool moving on the low side of the wellbore.

In certain instances, the 3D geometric model 215 can be operatedconcurrently with the real-world operations it is modeling and provideinformation on passage of the tool string and the forces involved atapproximately the same time, accounting for time taken to perform thecomputing, that the modeled tool passage forces are happening in thereal-world. For example, the model 215 may be operated to model thereal-world in real time.

The 3D geometric model 215 uses the inputs 240 and/or inputs from themathematical model 220 to determine the position of the tool string inthree-dimensional space relative to the well and the reaction forcesbetween the tool string and the contacting surfaces of the well, bothaccounting for deflection of the tool string. The reaction forces caninclude frictional forces, as well as contact force due to gravity anddeformation of the tool string, for example, from traversing a bend inthe well, from buckling of the tool string and/or other. Other forcescan be accounted for. The 3D geometric model 215 then, using the 3D dataabout the surfaces of the tool string and the well, simulates passage ofthe 3D surfaces of the tool string across the corresponding surfaces ofthe well when subjected to the calculated forces and determines how thesurfaces interact. In this analysis, the 3D geometric model 215 canidentify where features of the tool string (upstanding above or recessedbelow surface roughness) engage features of the contacting well surfaces(upstanding above or recessed below surface roughness), and can simulatethat interaction to determine the force the interfacing surfacescontribute to the total force necessary to move the tool string, ortools thereof, in a specified direction (e.g., uphole, downhole,rotationally, and/or other). The analysis takes into account the contactarea between the features, the contact angle of the interfacing shapesof the features, the stiffness of the features and the remaining toolstring and well, the frictional coefficient between the features andother characteristics. The analysis can further determine whether theinteraction will cause the tool string to lock with the well in a statethat would prevent movement in the specified direction being analyzed,given the total force necessary to move the tool string, the availableforce to move the tool string, and/or move the portion thereof, and/orthe specified maximum stresses of tool string and/or well surfaces. The3D geometric model 215 can further perform the analysis on specifiedportions of the tool string, for example corresponding with a tool or aportion of a tool in the tool string.

The 3D geometric model 215 provides the information above in outputs255. The outputs 255 include the values of such information in the formof single outputs, tables and graphs. In certain instances, the outputs255 include values of the calculated information correlated to thelocations in the interval to which they relate, for example, forceversus depth tables or graphs, deformation versus depth tables orgraphs, and other. The information can yield a surface tubing/cableforce profile indicating the forces needed to be applied to thetubing/cable at the surface rig to push and/or pull the tool stringthrough specified locations in the interval of the wellbore, given thefrictional and other forces acting on along the length of the toolstring and/or line resisting the surface force. Correspondingly, theinformation can yield the force realized, in pull and/or push, in thetool string or line supporting the tool string at specified locationsalong the length of the well, including at the line-to-tool stringconnection, given the force input at the surface and the frictional andother forces acting along the length of the tool string and/or lineresisting that force. The geometric model 215 can also determine theaxial deformation of the tool string and/or line supporting the toolstring at specified locations in the interval due to the applied forces.

Some example outputs 255 include effective deployment probability,surface cable tension profile, downhole cablehead tension profile,maximum flow/injection rate profile, maximum overpull profile, cablestretch profile, well contact force profile, axial effective forceprofile, 3D ID profile, and/or others.

In evaluating operation of the tool string and tools thereof, the 3Dgeometric model 215 can determine the forces involved in moving aportion of a tool in the tool string at a specified location in thewell, accounting for the interaction of features on the end and lateralfacing surfaces of the tool string and well. For example, in the contextof the tool having a portion that is or can potentially be in contactwith a surface of the well and that moves relative to the well, thereaction forces between the moving portion of the tool and the well canaffect the force available to act on and move the moving portion.Additionally, the reaction forces between other portions of the toolstring and the surfaces of the well can affect the force available toact on and move the moving portion of the tool whether or not the movingportion of the tool is or can potentially be in contact with a surfaceof the well. The moving portion of the tool may also be coupled to anactuator that provides force to move the moving portion (e.g.,hydraulic, electric, pneumatic, spring and/or other type of actuator).

Other components in the tool string and their arrangement in the toolstring, for example, due to their weight, damping/stiffnesscharacteristics, dynamic characteristics including if and how they aremoved relative to the moving portion (e.g., dropped, pushed or pulled)and other characteristics, may affect the force available to act on andmove the moving portion. Finally, external forces acting on the toolstring and/or tool can affect the force available to act on and move themoving portion. In evaluating operation of the tool string, the 3Dgeometric model 215 can account for each of these factors and calculatethe net force required and/or available to move the moving portion. Forexample, in the context of a jarring tool or a setting tool, the 3Dgeometric model 215 can determine the jarring force or the setting force(uphole and/or downhole) the tool can provide when operated at aspecified location in the interval of the well. The information can beoutput as outputs 260.

Finally, in certain instances, the 3D geometric model 215 can beoperated concurrently with the real-world operations it is modeling toperform some or all of the analysis described above and provide theoutputs 255 at approximately the same time, accounting for time taken toperform the computing, that the information output is occurring in thereal-world.

Additionally, because the 3D geometric model 215 operates on 3D data,the 3D geometric model 215 can provide, as outputs 255, a display with areal-world looking graphical depiction of the tool string and well, andparticularly, a graphical depiction of how the features of eachinteract. FIGS. 4A and 4B, discussed below, are examples of such agraphical depiction. In certain instances, the display can depict thetool string and well interaction, and forces involved, at approximatelythe same time it is happening in the real-world. For example, the 3Dgeometric model 215 can display the tool string and well interactions,and forces involved, in real time. This allows the operator to followalong as the tool string is being inserted or withdrawn from the well,visualize the interactions between the tool string and well, andevaluate and address any issues at approximately the same time as it ishappening in the real-world.

FIG. 4A shows an example display with a real-world looking depiction 400of the tool string 415 and well 420 that can be an output 255. In theexample, a blunt leading end face of a tool string 415 is encounteringan abrupt reduction in diameter of the inner surface of the wellcompletion tubular 420 at a tool/well interface labeled 410 by thesystem as “Fail.” The leading end face abuts the abrupt reduction indiameter that increases the force necessary to move the tool string 415downhole (to the left of the figure) relative to the well completiontubular 420, and may lock the tool string 415 against further downholemovement. Here, the tendency for the tool string 415 to engage with thetubular 420 is exacerbated because the tool string 415 contains aknuckle joint that has allowed the leading portion of the tool string415 to drop into the increased diameter portion of the tubular 420. The3D geometric model 215, takes into account the stiffness of the toolstring 415, including the depicted knuckle joint and its position in thetool string 415. The display allows an operator to visualize theinteraction between surfaces of the tool string 415 and well completiontubular 420 to better understand what aspects of the interaction arecausing the problems, and to evaluate whether the 3D graphical model'sanalysis is correct.

FIG. 4B shows another example display 450 with a real-world lookingdepiction of the tool string 455 and well 475 that can be an output 255.In the example, the well 475 has a wellbore portion 485 with a largedegree of helical or sinusoidal buckling, and due to the stiffness ofthe tool 455, the tool is unable to pass. The beginning of the bucklingis labeled 470 by the system as “Fail.” As above, this display allows anoperator to visualize the interaction between the surfaces of the toolstring 455 and the well 475 to better understand what aspects of theinteraction are causing the problems, and to evaluate whether the 3Dgraphical model's analysis is correct.

The illustrated tool passage modeling system 200 also includes theadaptive machine learning model 210, which receives inputs 250 andprovides outputs 270 based on the inputs 250 and data retrieved from ahistory store 235 (e.g., database or repository). Generally, theadaptive machine learning model 210 utilizes historical data stored inthe history store 235 (e.g., geometric and solid model data for welltool strings, geometric data for wellbore designs, wellbore trajectory(MD, INC, AZI), historical downhole tool forces measured downhole(accelerometers, tension/compression) and tool passage data based on thecombination of such solid model data and designs) to predictivelydetermine, for instance, tool passage success, tool passage successprobability, and other outputs. Generally, the adaptive machine learningmodel 210 is a learning machine having “artificial intelligence” thatutilizes algorithms to learn via inductive inference based on observingdata that represents incomplete information about statistical phenomenonand generalize it to rules and make predictions on missing attributes orfuture data. Further, the adaptive machine learning model 210 mayperform pattern recognition, in which the adaptive machine learningmodel 210 “learn” to automatically recognize complex patterns, todistinguish between exemplars based on their different patterns, and tomake intelligent predictions on their class.

At a high level, the adaptive machine learning model 210 may retrieveinputs 250 (e.g., measurement values and recorded tension, accelerometerand other forces from logging data, tool and cable movementcharacteristic information), data from the tools/well/fluidsspecifications store 230, and data from a history store 235 to performclustering and classification in characterizing borehole trajectory,geometry and feasibility of deployment (e.g., passage of the well toolstring through a portion of the wellbore or other tubular). The adaptivemachine learning model 210 may also, based on the inputs 250 and/or datafrom the stores 230 and 235, interpret deployment simulation results andgenerate graphical outputs depicting such results.

In some embodiments, the adaptive machine learning model 210 comprisesan artificial neural network machine learning system that includes analgorithm of interconnected nodes, where each node is a sub-algorithmthat performs data manipulation on inputs (such as inputs 250). Theinterconnections between the nodes may be directed so that data from onenode is directed to a specific subset of the other nodes, and weightedto influence how the data is operated on by the receiving node. Theartificial neural network may be calibrated by providing it an input andthe desired output, and the neural network operates to adjust the pathof the interconnections and their weights (via back propagation) so thatnext time it receives the input it will output the desired output. Byproviding the neural network multiple inputs and their correspondingdesired outputs, it eventually learns an algorithm that will yield thedesired output for each input. Given an entirely new input, the neuralnetwork may effectively predict what the output should be.

In some embodiments, the adaptive machine learning model 210 comprises asupport vector machine (SVM) that analyzes data and recognize patterns,and may be used for classification and regression analysis. For example,the adaptive machine learning model 210 may receive the inputs 250 andpredict, for each given input 250, which of two possible classescomprises the input 250. In other words, the adaptive machine learningmodel 210 as an SVM may be a classifier that provides a binary output(e.g., tool passage or no tool passage). Typically, a support vectormachine constructs a hyperplane or set of hyperplanes in a high- orinfinite-dimensional space, which can be used for classification,regression, or other tasks.

Some example inputs 250 include: tool length, effective deploymentprobability, tool OD profile, downhole cablehead tension profile,surface cable tension profile, tool OD profile, historical comparisonanalysis, tool weight, maximum flow/injection rate profile, well contactforce profile, multiple tool string deployment probability, cablediameter, maximum overpull profile, axial effective force profile, cablediameter, optimized tool string design from constrained components,cable stretch coefficient, cable stretch profile, tool length, orderedtool suggestions from constrained string options, cable breakingstrength, well contact force profile, tool OD profile, cable breakingstrength, surface cable tension profile, axial effective force profile,borehole diameter profile, cable drum crush caution, borehole fluiddensity, 3D ID profile, cable drum crush caution, borehole fluidviscosity, borehole temperature profile, cablehead weak point design,allowable % breaking strength, borehole coefficient of friction profile,well trajectory profile (MD, INC, AZI), borehole roughness, trajectoryeccentricity, borehole temperature profile, tool stress limitations, RIH(“run in hole”)/POOH (“pull out of hole”) running speed, toolflexibility—flex joints, tool standoff profile, tool decentralizerprofile, tool centralizer profile, surface pressure, toolmobility—roller boogie profile, wellhead friction, formation compactionprojected dynamic profile, flowrate for gas and liquids, thermal inducedmechanical distortion profile, 2D ID profile, maximum flow/injectionrate profile, wellbore corkscrew characteristics, previous deploymenthistory in same wellbore, previous deployment history in similarwellbores, previous deployment history of all wellbores in database,tool accelerometer profile (RIH/POOH), 3D multi-finger mechanicalcaliper measurements, downhole camera optical survey, opticalinterpretation from downhole camera survey, other sensors or methods ofevaluation, multiple tool string candidate designs for individualevaluation, and/or subsurface geomechanical dynamic predictions.

As described above, the illustrated tool/well/fluids specification store230 includes a database of tools or other components that could be usedin the tool string, for example identified by manufacturer, modelnumber, size and pressure rating, correlated to their characteristics.The tools string characteristics may be retrieved by the adaptivemachine learning model 210 from the data store 230 based on the inputs250, for example. The well characteristics include information about thewellbore and the components, such as casing and completion stringcomponents, installed in the wellbore that make up the well. In certaininstances, the geometric characteristics of the wellbore can be obtainedfrom survey data, such as survey logs (having information on inclinationrelative to gravity and direction per depth), caliper logs (diameter perdepth) and other data, and retrieved by the adaptive machine learningmodel 210. The geometric characteristics of the components installed inthe wellbore can include the outside and inside diameters of thecomponents at different positions along the length of the well, thelengths of the components, the types of components, their order in thewell, the type of connection or other interface between the components,flow restrictions through the components, component weights, and otherinformation. The well characteristics can also include materialproperties such as the types of material of the well components, theyield and plastic strength and elastic modulus of the materials, thefrictional characteristics of the materials and other information. Incertain instances, the well characteristics can include additionalinformation beyond geometric and material characteristics.

The adaptive machine learning model 210 may also retrieve, from the datastore 230, data including fluid characteristics about the fluids in thewell and the tool string based on the inputs 250. As described above,some examples can include static characteristics such as fluid type(e.g., gas, liquid, mixture), fluid viscosity, fluid density, pressuresinside and out of the tool string and within the well at the surface and(if available) downhole, temperatures at different portions along thelength of the well and other information. The fluid characteristics canalso include dynamic characteristics such as flow rate, pressures insideand out tool string and within well, and other information.

The illustrated history store 235 includes data such as geometric andsolid model data for well tool strings, geometric data for wellboredesigns, and tool passage data based on the combination of such solidmodel data and designs for previous RIH/POOH operations. For instance,combinations of geometric and solid model data for well tool strings andgeometric data for wellbore (or other tubular) designs that havepreviously resulted in successful RIH/POOH operations (e.g., successfulpassage of a well tool string or elongate well tool through a portion ofa wellbore or tubular) may be stored in the history store 235.

In a first step of a process performed by the adaptive machine learningmodel 210, inputs 250 may be received, for example, from a user or welloperator. Such inputs 250 may define, for instance, well toolcomponents, wellbore fluids, and information regarding the wellbore orother tubular. Based on such inputs 250, additional characteristicsabout the well tool or tool string to be modeled may be retrieved fromthe data store 230, as well as additional information of the fluidsand/or wellbore or tubular to model. Next, the adaptive machine learningmodel 210 may search the data stored the history store 235 for matches(or “next-best” matches) to the specified and/or retrieved dataregarding the tool string, wellbore, and/or fluids. For example, theadaptive machine learning model 210 may search and find instances ofgeometric and solid model data for well tool strings in the historystore 235 that most closely resemble (or match) tool string dataprovided by inputs 250 and/or retrieved from the data store 230 based onthe inputs 250. The adaptive machine learning model 210 may also searchand find instances of geometric data for wellbore designs in the historystore 235 that most closely resemble (or match) wellbore (or othertubular) data provided by inputs 250 and/or retrieved from the datastore 230 based on the inputs 250.

In some embodiments, based on the history matching process describedabove, the adaptive machine learning model 210 may provide, through theoutputs 270, a classification of the tool string provided by the userthrough the inputs 250 into one of two classes: passable through thewellbore as described by the inputs 250 or not passable through thewellbore as described by the inputs 250. In some embodiments, beyondsuch a binary classification, the adaptive machine learning model 210may provide for a statistical probability of passage of the input toolstring through the wellbore (e.g., 50% success, 75% success) based onthe history matching process. In some embodiments, the outputs 270 mayalso include a graphical representation (e.g., as shown in FIGS. 4A-4B)of the classification determined by the adaptive machine learning model210.

As discussed above, the modules of the modeling system 200 can be usedseparately and/or together in modeling the effectiveness of theoperation of a tool string. By way of example, FIG. 5 illustrates anexample well tool string 500 that the modeling system can be used inmodeling the effectiveness of operation. In some instances, the welltool string 500 may be all or a portion of the well tool string 125shown in FIG. 1. In some embodiments, the well tool string 500 may be asubsurface line deployed tool string, operable to set, pull, orotherwise service subsurface devices in the well by applying an axialjarring force to the devices.

As illustrated, the well tool string 500 includes a wireline socket 505,a stem 510, one or more jars 515, a knuckle joint 520, and a pullingtool 525. The wireline socket 505 provides a connection of the line(e.g., a slickline, braided line, or other wireline) to the well toolstring 500. The pulling tool 525 provides a connection to another devicein the well (i.e., the device begin jarred). The stem 510 providesweight to the well tool string 500 to work in connection with jars 515to apply an impact force, oriented uphole and/or downhole, to the devicein the well. The jars 515 telescope in and out axially to allow theportion of the tool string 500, including the wireline socket 505, stem510, and uphole portion of the jars 515, to be rapidly lifted uphole toapply an uphole impact load through the pulling tool 525 to the device,or lifted uphole and dropped downhole to apply a downhole impact loadthrough pulling tool 525 to the device. The stem 510 may be designedwith a particular size (e.g., length, outer diameter) and weight basedon an impact force required to, for example, set subsurface controls orurge the well tool string 500 through the well. In certain instances,the impact force of the jars 515 may be assisted by springs and/orhydraulics in the jars 515 or as a part of an accelerator tool (notshown) in the tool string 500. In certain instances, the jars 515 may bedetent jars that include a detent mechanism that retains the jar axiallycontracted until subjected to a specified uphole force that overcomesthe detent mechanism. The knuckle joint 520, generally, include a balland socket mechanism that allows improved angular bending mid-toolstring.

The outer diameter of the jars 515 may drag on the well surfaces, andfriction with the well surfaces and other forces such as fluidic forces,gravity, and friction on the line to the surface, react against movementof the jars 515 increasing the force needed to axially telescope thejars 515 in and out, and decreasing the impact load the jars 515 canprovide. The modeling system 200 (FIG. 2) can determine the forces thatreact against the telescoping movement, and allow the operator toevaluate multiple different configurations of the tool string 500 toselect the tool string configuration that can achieve a specified ormaximum force applied by the jarring operation to the device in thewell. The mathematical model 220, 3D geometric model 215, and/oradaptive learning model 210 can be used individually or in concert toevaluate the operation of different tool string configurations.

The modeling system 200 can account for the effect and placement ofcertain tools in the tool string 500, including the stroke and springconstant of accelerators, the stroke and release force of detent jars,wire stretch relationship to detent jars, stem location and weight, wellinclination and resulting gravitational and frictional forces, linetension at the surface versus line tension at the socket 505, themomentum of moving portions of the tool string 500 as affected by forcesresisting the movement and/or other aspects accounted for by themodeling system 200. For example, the operator may use the modelingsystem 200 in selecting the release force of the detent jars by modelingthe line tension at the socket 505, and selecting the release force ofthe detent jars in relation to the forces acting on the jar 515 and themaximum allowable tension at the socket 505. Additionally, the operatormay use the modeling system 200 to determine the effect that selecting aparticular detent jar release force will have on the impact force thatthe tool string 500 is able to produce in given well conditions. Theoperator can test other configurations of the well string 500 using themodeling system 200, for example having different weight stems,accelerators of different spring rate and stroke, and otherconfigurational changes that effect the impact force that the toolstring 500 is able to produce in the given well conditions. The operatorcan then select, for example, the configuration of tool string 500 thatproduces the greatest application of jarring force to the device in thewell.

Turning now to FIGS. 6-8, some example methods in operation of themodeling system are described.

FIGS. 6A-6B illustrate flowcharts describing example methods 600 and650, respectively, for modeling passage of a well string through aportion of a well. Methods 600 and 650 may, in some embodiments, beimplemented by the 3D modeling system 185 illustrated in FIG. 1.Alternatively, methods 600 and 650 may be implemented by the toolpassage modeling system 200 illustrated in FIG. 2, which may implement,for example, a mathematical model, a 3D geometric model, and an adaptivemachine learning model, as shown in FIG. 2.

Example methods 600 and 650 are shown as having a number of steps thatmay or may not be performed in the order depicted in the flow chart. Incertain instances, some of the steps may be omitted and/or repeated andother steps may be added.

In step 602, inputs are received, for example into a 3D modeling system,representing geometric characteristics of a well string. In someembodiments, the geometric characteristics may include a length of thewell string or lengths of particular components of the well string, aswell as diameters of the well string and particular components of thewell string. In some embodiments, the input may specify a particularwell string (e.g., by manufacturer, by type of well string component, orotherwise), and the 3D modeling system may receive specific geometriccharacteristics from for example, a data store, for the particular wellstring. The geometric characteristics may include, for example,dimensions such as diameters and otherwise, of outwardly facing lateralfaces of the well string.

In step 604, the 3D modeling system receives inputs representinggeometric characteristics of a well. For example, the geometriccharacteristics of the well may include one or more diameter dimensionsof the well (or other tubular structure, such as a casing), as well asone or more lengths associated with the well. For example, there may bedifferent diameter portion of the well with each diameter portion havinga particular length. In some embodiments, the inputs received in steps602 and 604 may be received by the 3D modeling system through agraphical user interface, such as the graphical user interface 300 shownin FIG. 3A.

In step 606, the 3D modeling system compares the geometriccharacteristics of the well string and the geometric characteristics ofthe well. In some embodiments, comparison of such geometriccharacteristics may include a comparison of particular contact pointsfor surfaces between the well string in the well. For example, the 3Dmodeling system may compare a solid 3D model of the well string with a3D model of the well based on the geometric characteristics of the wellstring and well. The solid model of the well string may be derived andor calculated based on stored information about the well string forparticular components of the well string.

In step 608, the 3D modeling system may determine a prediction of aforce to pass the well string through the well based on the comparisonin step 606. For example, in comparing the geometric characteristics ofthe well string and the geometric characteristics of the well, the 3Dmodeling system may determine that contact between at least one surfaceof the well string and a surface of the well may occur. Based on thecomparison, the 3D modeling system may determine the magnitude of forcesrequired to move the well string through the well and past the contactpoint of the surfaces.

In step 610, the 3D modeling system may determine whether the wellstring will pass through the well based on the comparison step 606. Forexample, in some embodiments, the 3D modeling system may determinewhether there is sufficient space within the well to pass the wellstring through given the geometric characteristics of the well stringand the well. Further, the predicted force (or forces) determined instep 608 may also determine, at least in part, whether the well stringcan pass through the well. For example, if the predicted force (orforces) is greater than a specified failure force of the well string,passage of the well string through the well may not be possible.

In step 612, the 3D modeling system determines a radial position of thewell string relative to the well due to loads on the well string. Forexample, due to fluid forces, wellbore forces, or other loads on thewell string, the well string may be shifted radially within the well.

In step 614, the 3D modeling system may generate an image of the wellstring in the well depicting contacting surfaces of the well string andthe well. For example, based on the comparison of the geometriccharacteristics of the well string and the geometric characteristics ofthe well in step 608, the 3D modeling system may determine contactpoints for contact surfaces between the well string in the well. Suchcontact points and contact surfaces may be depicted graphically in arepresentative image of the well string in the well. For example, FIGS.4A-4B show such a representative image of a well string in a welldepicting contacting surfaces of the well string in the well.

In step 616, the 3D modeling system determines whether it receives acurrent location of the well string within the well. If the 3D modelingsystem receives the current location of the well string in the well instep 616, then in step 618, the 3D modeling system may generate an imageof the well string in the well depicting the contacting surfaces of thewell string in the well at the current location.

If the 3D modeling system does not receive the current position of thewell string in the well at step 616, or after completion of step 618,the method 600 may continue to step 620. In step 620, the 3D modelingsystem may determine whether it receives geometric characteristics of atubing string having the well string deployed thereon. For example, insome embodiments, the well string may be deployed on a tubing string,such as a coiled tubing string or straight threaded tubing string.

If the 3D modeling system receives the geometric characteristics of thetubing string in step 620, then in step 622, the 3D modeling systemcompares the geometric characteristics of the well string, the tubingstring characteristics, and the geometric characteristics of the well.In step 624, the 3D modeling system may then determine a force to passthe well string through the well based on the comparison of the wellstring characteristics, tubing string characteristics, and well data.

If, in step 620, the 3D modeling system does not receive the geometriccharacteristics of the tubing string, or after completion of step 624,method 600 continues with step 626. In step 626, the 3D modeling systemdetermines whether it receives characteristics of a line supporting thewell string in the well. For example, in some embodiments, the wellstring may be deployed on an electric line, such as a slickline orbraided line or other type of wireline.

If the 3D modeling system receives the characteristics of the line instep 626, then in step 628, the 3D modeling system compares thegeometric characteristics of the well string, the line characteristics,and the geometric characteristics of the well. In step 630, the 3Dmodeling system may then determine a force to pass the well stringthrough the well based on the comparison of the well stringcharacteristics, the line characteristics, and the well data.

Turning to FIG. 6B and method 650, this example method may start at step652 when a user of a 3D modeling system inputs geometric characteristicsof a first configuration of a well string for applying force to a welldevice. For example, in some embodiments, the user may utilize agraphical user interface, such as the graphical user interface 300 shownin FIG. 3A, to input the geometric characteristics of the firstconfiguration of the well string. Such inputs can include, for example,specific geometric dimensions, such as lengths and diameters, of thefirst configuration of the well string. Alternatively, the user mayinput component names or other information (e.g., manufactureinformation or otherwise) into the graphical user interface and the 3Dmodeling system may receive specific geometric information based on theinput (e.g., from the solid model store 225, the tool/well/fluidsspecification store 230, or other repository).

In step 654, the user inputs geometric characteristics of a secondconfiguration of a well string for applying force to a well device. Forexample, inputs for the second configuration may be implemented into the3D modeling system in similar fashion as the input for the firstconfiguration. In some embodiments, the force applied by either of thefirst configuration of a well string for the second configuration of awell string may actually or otherwise operate the well the box (e.g., aPacker, a plug, or other downhole device).

In step 656, the user inputs geometric characteristics of aconfiguration of the well. For example, the geometric characteristicsmay include various diameters of the well (or other tubular), as well aslengths of all or portions of the well. The geometric characteristicsmay also include specific information about particular irregularities(e.g., crevices, turns, jogs, dog legs, or otherwise) as determined, forexample, by a caliper system.

In step 658, the user initiates a determination of a prediction of aforce to pass the first configuration of a well string through the well(or at least a particular portion of the well). This initiation mayinclude initiating the 3D modeling system to determine the force throughthe graphical user interface. Step 658 also includes initiatingdetermination of a prediction of the force to pass the secondconfiguration of a well string through the well based on the inputs ofsteps 652, 654, and 656.

In step 660, the user may then accumulate two or more components of theparticular configuration (i.e., the first configuration or the secondconfiguration) of a tool string indicated as having the lower predictedforce to pass through the configuration of the well. For example, afterthe user initiates determination of the prediction of forces to pass thefirst and second configurations of a well string through the well, the3D modeling system may determine such forces and provide thedetermination of such forces to the user (e.g., graphically, textually,or otherwise).

In step 662, the user may then input geometric characteristics of aconfiguration of a second well. In some embodiments, the second well mayhave different characteristics, such as different geometriccharacteristics, as compared to the well defined in 656. For example,the configuration of the second well may have different diameters ofwellbore, casing, or other tubulars, as well as, for example, adifferent vertical depth as compared to the first well. As anotherexample, the configuration of the second well may be a directional well,while the configuration of the first well defined in step 656 may be asubstantially vertical well. As another example, the configuration ofthe second well and the configuration of the first well may both bedirectional wells, but the second configuration may have a tighterradius between a substantially vertical portion and a substantiallyhorizontal portion of the well as compared to the configuration of thefirst well.

In step 664, the user initiates a determination of a prediction of theforce to pass the first configuration of the well string through thesecond well. Step 664 also includes an initiation, by the user, of adetermination of a prediction of the force to pass the secondconfiguration of the well string through the second well. Thedetermination may be made by the 3D modeling system based on, forexample, the inputs provided in step 652, 654, and 662.

In step 666, the user may then accumulate two or more components of theparticular configuration of tool string (i.e., the first configurationor the second configuration) indicated as having the lower predictedforce to pass through the configuration of the second well.

FIGS. 7A-7B are flowcharts describing the example method 700 and 750,respectively, for modeling operation of a well tool in applying a forceto a device in a well. Method 750 may, in some instances, be implementedby the 3-D modeling system 185 illustrated in FIG. 1, by the modelingsystem 200 illustrated in FIG. 2, and/or another modeling system.

Example methods 700 and 750 are shown as having a number of steps thatmay or may not be performed in the order depicted in the flow chart. Incertain instances, some of the steps may be omitted and/or repeated andother steps may be added.

In method 700, step 702, a computing system receives inputs representinggeometric characteristics of a well tool for applying force to a devicein a well. The device in a well can be a number of different devices.For example, in the context of a jarring operation where the well toolis a jar, the device might be tool actuated in response to the jarringforce, a tool or other item lodged in the well that will be jarredloose, or another device. In certain instances, geometriccharacteristics of the well tool can be stored in a data store, such asthe solid model store 225 and/or tool/well/fluid specification store 230shown in FIG. 2. In certain instances, the geometric characteristics ofthe well tool can be manually entered, input from another system, and/orinput in another manner. Other characteristics of the well tool can alsobe received by the computing system.

In step 704, the computing system also receives inputs representinggeometric characteristics of the well. As above, in certain instances,the geometric characteristics of a well can be stored in a data store,such as the solid model store 225 and/or tool/well/fluid specificationstore 230 shown in FIG. 2. In certain instances, the geometriccharacteristics of the well can be manually entered, input from anothersystem and/or input in another manner. Other characteristics of the wellcan also be received by the computing system.

In step 706, the computing system compares the geometric characteristicsof the well tool and the geometric characteristics of the well, and instep 708, determines a predicted reaction force on the well tool due tocontact of a surface associated with the well tool and a surface of thewell. The reaction force is of a nature that affects operation of thewell tool. In the context of a jarring tool, the reaction force maycounter the impact force produced by the jar. In certain instances, thereaction force is a frictional force acting between the well and thesurface associated with the well tool. In certain instances, the surfaceassociated with the well tool is on the well tool itself, in otherinstances surface can be a surface of another component that is coupledto the well tool, such as other components of the tool string, the linesupporting the tool string, and/or other.

In step 710, the computing system can determine a predicted net forcethat can be applied by the well tool to the device. In certaininstances, the net force is a function of the total amount of force thatthe well tool can apply to the device less the predicted reaction force.The determination of the net force can take other characteristics intoaccount, including contributions of force provided by other componentscoupled to the well tool.

In step 712, the computing system receives inputs representing geometriccharacteristics of a second well tool for applying force to the device.In certain instances, the second well tool is another well tool in thetool string. In the context of a jarring tool, the second tool may beanother jarring tool, accelerator, a stem and/or another tool. As above,in certain instances, the geometric characteristics of the second welltool can be stored in a data store, such as solid model store 225 and/orthe tool/well/fluid specification store to 30 shown in FIG. 2. Incertain instances, the geometric characteristics of a well can bemanually entered, input from another system and/or input in anothermanner. Other characteristics of the second well tool can also bereceived by the computing system.

In step 714, the computing system compares the geometric characteristicsof the second well tool and the geometric characteristics of the well,and in step 716, determines a predicted reaction force on the well tooldue to contact of a surface associated with the second well tool and asurface of the well. The reaction force is of a nature that affectsoperation of the well tool. In the context of a jarring tool, thereaction force may counter the impact force produced by the jar. Incertain instances, the reaction force is a frictional force actingbetween the well and the surface associated with the well tool. Incertain instances, the surface associated with the second well tool ison the second well tool itself, and in other instances, the surface canbe a surface of another component that is coupled to the second welltool, such as other components of the tool string, the line supportingthe tool string, and/or other.

In step 718, the computing system can determine a predicted total netforce that can be applied by the first and second well tools to thedevice. In certain instances, the net force is a function of the totalamount of force that the well tools can apply to device less thepredicted reaction forces. The determination of the net force can takeother characteristics into account, including contributions of forceprovided by other components coupled to the well tool.

In step 720, inputs representing geometric characteristics of a toolstring are received by the computing system. Then in step 722 thecomputing system can additionally or alternatively compare the geometriccharacteristics of the well screen and the geometric characteristics ofthe well. In step 724, the computing system can determine a predictedreaction force due to contact of the well string with the well. Asabove, the predicted reaction force determined in step 724 is of anature that effects operation of the well tool.

In this manner, the operation of a well tool is modeled in applying aforce to a device in a well.

In method 750, step 752, geometric characteristics of the firstconfiguration of well string for applying force to a well device in awell are input into a computing system. As above, the device can be anumber of different devices. Also as above, the geometriccharacteristics of the well tool can be stored in a data store, such asthe solid model store 225 and/or the tool/well/fluid specification store230 shown in FIG. 2. The user can access these data stores via a GUI. Incertain instances, the geometric characteristics of the well tool can bemanually entered, input from another system, and/or input in anothermanner. Other characteristics of the well tool can also be input intothe computing system.

In step 754, geometric characteristics of a second configuration of wellstring for applying force to the well device are input into thecomputing system. As above, the geometric characteristics can be inputfrom a data store, manually entered, input from another system, and/orinput another manner. Other characteristics of the second configurationwell tool can also be input into the computing system. In step 756,geometric characteristics of the configuration of well that contains thedevice are input into the computing system. As above, the geometriccharacteristics can be input from a data store, manually entered, inputfrom another system, and/or input and another manner. Othercharacteristics of the well can also be input into the computing system.

In step 758, the computing system is initiated to determine a predictionof the force that the first configuration of the well string is capableof applying to the device, and a prediction of the force that the secondconfiguration of well string is capable of applying to the device. Basedon this information, components of the configuration of well stringindicated by the computing system as capable of applying the higherpredicted force are accumulated, for example, to build suchconfiguration of the well string and/or to ship the components of suchconfiguration of well string to customer.

FIGS. 8A-8B illustrate flowcharts describing example methods 800 and850, respectively, for modeling passage of a well tool through a portionof a well using an adaptive machine learning model. Methods 800 and 850may, in some embodiments, be implemented by the 3D modeling system 185illustrated in FIG. 1. Alternatively, methods 800 and 850 may beimplemented by the tool passage modeling system 200 illustrated in FIG.2, which may implement, for example, an adaptive machine learning model,such as the adaptive machine learning model 210 illustrated in FIG. 2.

In step 802, the adaptive machine learning model receives a first set ofinputs representing characteristics of a well tool. The adaptive machinelearning model also receives a second set of inputs representingcharacteristics of a well. In some embodiments, the adaptive machinelearning model may be a neural network executed on a computing system,such as the computing system 150 shown in FIG. 1.

In step 804, the adaptive machine learning model receives historicaldata representing a plurality of other well tools passed through aplurality of other wells. For example, in some embodiments, thehistorical data may be stored in a history store, such as the historystore to 235 shown in FIG. 2. Alternatively, the historical data bestored in any appropriate database or repository communicably coupled tothe adaptive machine learning model. For example, the historical datamay be stored as the data 195 in the repository 190 shown in FIG. 1.Step 804 also includes receiving historical data representing aplurality of characteristics of the other well tools and the otherwells.

In step 806, the adaptive machine learning model matches the historicaldata with at least a portion of the first and second sets of inputs. Forexample, characteristics of the well tool, such as geometriccharacteristics of an outer surface of the well tool (e.g., lateralouter surfaces of the well tool) may be compared to historical datarepresenting geometric characteristics of the other well tool passed theother well. In addition, the characteristics of the well maybe nexthistorical data representing characteristics of the other well. Suchcharacteristics may include, for example, information regarding wellborediameter, wellbore shoulders, wellbore cavities, and other geometriccharacteristics of the well and the other wells.

In step 808, the adaptive machine learning model determines whether thewell tool may pass through the interval of the well based on thematching of the first and second sets of inputs and the historical data.For example, in some embodiments, the adaptive machine learning modelperforms historical matching to determine whether the well tool can passthrough the interval of the well based on, for example, previous similarwell tools that passed through other wells of similar or identicalcharacteristics as the well defined in step 802. More specifically, theadaptive machine learning model may look for similar of the other wellswith similar characteristics of the well defined in step 802, and mayalso look for similar other well tools with similar characteristics(e.g., geometric or shape characteristics) of the well tool defined instep 802. Based on a determination that the other similar well toolshave passed through the other similar wells, the adaptive machinelearning model may determine that the well tool passes through theinterval the well.

In step 810, the adaptive machine learning model may determine apredicted reaction force on a portion of the well tool due to contactbetween a surface associated with the well tool and a surface of thewell. For example, based on the matching of historical data with theportion of the first and second sets of inputs, the adaptive machinelearning model may predict contact between a surface associated with thewell tool, such as a lateral facing surface, and the surface of thewell, such as the wellbore (or other tubular surface). In determiningsuch contact, the adaptive machine learning model may predict, based onthe historical matching, the reaction force on the portion of the welltool. In some embodiments, the predicted reaction force may be equal toor substantially similar to the force necessary to urge the well toolthrough the interval of the well to overcome such contact between thesurface associated with the well tool and the surface of the well.

In step 812, the adaptive machine learning model determines whether aninput representing the specified failure force of the well tool isreceived. If the adaptive machine learning model determines that such aninput is received, in step 814, the adaptive machine learning modelreceives historical data representing a plurality of forces applied tothe other well tools passed through the other wells. For example, thehistorical data may include force data measured by, for example, sensorson the other well tools during passage of the other well tools throughthe other wells. Such historical force data may be stored and indexedby, for example, a particular location within the other wells in whichthe forces were measured on the other well tools.

In step 816, the adaptive machine learning model matches the historicaldata representing the plurality of forces and the input representing thespecified failure force of the well tool. In some embodiments, suchmatching may be a simple matching comparing force magnitude of theplurality of forces stored in the historical data to a force magnitudeof the specified failure force of the well tool. Alternatively, thematching may include a comparison of both force magnitude and forcevectors of the plurality of forces stored in the historical data withthe force magnitude and a force vector of the specified failure force ofthe well tool. Force vectors may include, for example, vectorsindicating an axial force (e.g., uphole or downhole) or a radial forceacting on the well tools.

In step 818, the adaptive machine learning model determines whether thewell tool can pass through the interval of the well based on thematching of the historical data representing the plurality of forces andthe input representing the specified failure force of the well tool. Forexample, if the matching of the historical data and the inputrepresenting the specified failure force indicates that the specifiedfailure force is much less in magnitude as compared to the historicallycollected force data, then the adaptive machine learning model maydetermine that the well tool cannot pass through the interval of thewell. For instance, the adaptive machine learning model may determinethat damage (e.g., catastrophic) to the well tool may occur based on thepredicted forces that will be applied to the well tool in the well thatare greater than the specified failure force of the well tool.Alternatively, if the specified failure force of the well tool is muchgreater in magnitude than the historical data representing the pluralityof forces, then the adaptive machine learning model may determine thatthe well tool can pass through the interval of the well.

If the adaptive machine learning model does not receive an inputrepresenting the specified failure force of the well tool in step 812,or once the adaptive machine learning model determines whether the welltool can pass through the interval of the well in step 818, method 800continues to step 820. In step 820, the adaptive machine learning modeldetermines whether it has received, from a mathematical model, adetermination whether the well tool can pass through the interval of thewell. For example, a mathematical model, such as the mathematical model220 shown in FIG. 2, may also make a determination separate from theadaptive machine learning model as to whether the well tool can passthrough the interval the well.

If the adaptive machine learning model receives a determination from themathematical model whether the well tool can pass through the intervalof the well, then the adaptive machine learning model compares themathematical model determination with its own determination of whetherthe well tool can pass through the interval of the well in step 822. Instep 824, the adaptive machine learning model, based on the comparison,adjusts a probability (e.g. a previously determined probability or a newprobability) of whether the well tool can pass through the interval ofthe well. For example, if the determination made by the mathematicalmodel agrees with the determination made by the adaptive machinelearning model, then a determined probability of whether the well toolcan pass through the interval of the well may be adjusted higher. If,however, determination made by the mathematical model does not agreewith the determination made by the adaptive e-learning model, then thedetermine probability of whether the well tool can pass through intervalof the well may be adjusted lower.

In some embodiments, the mathematical model, such as the mathematicalmodel 220, as well as a 3D geometric model, such as the 3D geometricmodel 215 shown in FIG. 2, may make determinations (e.g., independent orin conjunction) of whether the well tool can pass through the intervalof the well. In some cases, such determination may conflict. Forexample, the mathematical model determination may indicate that the welltool can pass through the interval of the well, while the 3D geometricmodel determination may indicate that the well tool cannot pass throughthe well. In such cases, the determination by the adaptive machinelearning model may act as a tiebreaker to make a final determination ofwhether the well tool can pass through the interval of the well, adjusta probability of whether the well tool can pass through the well,determine one or more reaction forces on the well tool as it passes (orattempts to pass) through the interval of the well, or otherwise. Forexample, a comparison may be made of the mathematical modeldetermination of whether the well tool can pass through the interval ofthe well with the 3D geometric model determination of whether the welltool can pass through the interval of the well. Based on the comparisonand on the adaptive machine learning model determination of whether thewell tool can pass through the interval of the well, a determinedprobability of whether the well tool can pass through the interval ofthe well may be adjusted (e.g. higher or lower). For instance, if boththe 3D geometric model and adaptive machine learning model determinethat the well tool cannot pass through the interval of the well, thenthe probability may be adjusted downward.

Continuing after step 824, or after a “no” determination in step 820, instep 826, the adaptive machine learning model receives a third set ofinputs representing characteristics of a second well tool. For example,the third set of inputs may represent geometric characteristics of thesecond well tool, such as, for example, geometric characteristics of oneor more outer, laterally facing surfaces of the second well tool.

In step 828, the adaptive machine learning model matches the historicaldata representing the other well tools passed through the other wellswith at least a portion of the third and second sets of inputs. In someembodiments, step 828 may be substantially similar to step 806, but theadaptive machine learning model compares characteristics of the secondwell tool and the well as opposed to the first well tool and the well.

In step 830, the adaptive machine learning model determines whether thesecond well tool can pass through the interval of the well based on thematching of the historical data with at least a portion of the third andsecond sets of inputs. Much like step 808, in step 830, the adaptivemachine learning model uses the comparative historical matching of thedata representing previous well tools passed through previous wells withcharacteristics of the second well tool defined in step 826 and thecharacteristics of the well defined in step 802.

In step 832, the adaptive machine learning model may determinerespective first and second probabilities of whether the first well tooland second well tool can pass through the interval of the well. Forexample, the first probability of whether the first well tool can passthrough the interval of the well may be based on historical dataindicating successes or failures of similarly-sized and shaped welltools (as compared to the first well tool) that have been deployed insimilar wells (as compared to the well defined in step 802). Likewise,the second probability of whether the second well tool can pass throughthe interval of the well maybe based on historical data indicatingsuccesses or failures of similarly-sized and shaped well tools (ascompared to the second well tool) that have been deployed in similarwells (as compared to the well defined in step 802). In many cases, thefirst and second well tool are different in that, even though they mayperform a similar or identical downhole function or operation (e.g.,actuating a downhole well tool or device), they may have differentgeometric characteristics (e.g., different lengths, different diameters,different shapes, and otherwise). Accordingly, a determination of thefirst and second probabilities may provide a well operator insight intowhich of the first or second well tools can more successfully passthrough the interval of the well to accomplish the desired function oroperation.

In step 834, the adaptive machine learning model may suggest one of thefirst or second well tools based on a greater of the first and secondprobabilities. For example, should the first well tool have a greaterprobability of success in passing through the interval of the well tool,the adaptive machine learning model may suggest this tool to a welloperator to accomplish the desired function or operation. In someembodiments, if the first and second probabilities are similar, oridentical, the adaptive machine learning model may present both optionsof the first and second well tool to the well operator along with theirrespective probabilities. The well operator can then make a selection ofthe first or second well tool based on an evaluation of the tools andtheir respective probabilities.

Turning now to method 850 shown in FIG. 8B, method 850 may begin at step852 when the user of an adaptive machine learning model executed on acomputing system inputs a first set of inputs representing a pluralityof geometric characteristics of a well string configuration operable toapply force to a downhole well tool and a well. For example, the usermay input the first set of inputs into a graphical user interface (GU),such as the graphical user interface 300 shown in FIG. 3A. In someembodiments, geometric characteristics may simply include a length ofthe well string and an average, or largest, outer diameter of the wellstring. Alternatively, the user may specify particular components (e.g.,by component name, manufacturer, or otherwise) and the adaptive machinelearning model may receive geometric data describing each component froma data store (such as the data stores 225 and/or 230). In someembodiments, the geometric characteristics may specify one or morediameters of each component of the well string. The geometriccharacteristics may also include a specific length of each component ofthe well string.

In step 854, the user may input a second set of inputs representingcharacteristics of the well. For example, the well characteristics mayinclude geometric characteristics of the well, such as one or morediameters of the well (or other tubular in the well) as well asgeometric characteristics of a wellbore wall of the well, such asshoulders, crevices, or other hang-up points along a surface of the well(e.g., as determined by MWD, LWD or caliper data). Additionalcharacteristics of the well may include lengths of one or more intervalsof the well, such as lengths of intervals with varying diameters.

In step 856, the user initiates operation of the adaptive machinelearning model to determine a prediction of the force that the wellstring is capable of applying to the downhole well tool. In someembodiments, initiating operation of the adaptive machine learning modelmay include initiating operation through a graphical user interfaceportion of the adaptive machine learning model, such as the interface300 shown in FIG. 3A.

In step 858, the user receives the prediction of the force. Theprediction of the force may be based on a match, by the adaptive machinelearning model, of at least a portion of the first and second sets ofinputs with historical data representing other well strings passedthrough other wells, as well as characteristics of the other wellstrings and the other wells. For example, the historical data mayinclude geometric and force data gathered during operations of the otherwell strings that are similar (e.g., geometrically, shape, components,and otherwise) to the well string.

In step 860, the user may receive a prediction of whether the wellstring can pass through the interval of the well based on the match bythe adaptive machine learning model of at least a portion of the firstand second sets of inputs with historical data representing other wellstring characteristics of the other well strings and the other wells.

In step 862, the user inputs a third set of inputs representinggeometric characteristics of a second well string configuration operableto apply a second force to the downhole well tool in the well. Forexample, much like step 852, the user can input additional inputsrepresenting characteristics of the second well string in order to, forexample, compare the first well string configuration with the secondwell string configuration. Such comparison may be useful in determiningwhich well string configuration can best accomplish the desired functionor operation (e.g., applying a force to the downhole well tool to, forexample, actuate the downhole well tool), while being able to passthrough the interval of the well.

In step 864, the user initiates operation of the adaptive machinelearning model to determine a prediction of the second force that thesecond well string is capable of applying to the downhole well tool.Thereafter, the user may receive the prediction of the second forcebased on a match by the adaptive machine learning model of a least aportion of the third and second sets of inputs the historical datarepresenting the other well strings passed through the other wells, aswell as the characteristics of the other well strings and the otherwells.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made. Accordingly, otherembodiments are within the scope of the following claims.

What is claimed is:
 1. A computer-implemented method for modelingoperation of a well tool in a well, the method comprising: receiving,with a computing system, inputs representing a plurality of geometriccharacteristics of the well tool, the well tool operable to apply aforce on a downhole well device by moving through the well relative tothe device; receiving, with the computing system, inputs representing aplurality of geometric characteristics of the well; determining, withthe computing system, based on the geometric characteristics of the welltool and the well, a predicted reaction force on a portion of the welltool that affects an operation of the well tool in applying the force tothe downhole device, the predicted reaction force generated at a contactbetween a surface associated with the well tool and a surface of thewell as the tool moves through the well while applying the force to thedownhole device; and comparing the predicted reaction force to a failureforce of the well tool to determine a success of the well tool at movingthrough the well while applying the force to the downhole device.
 2. Thecomputer-implemented method of claim 1, further comprising determining,with the computing system, a net predicted force that can be applied bythe well tool to the device in the well based on the predicted reactionforce.
 3. The computer-implemented method of claim 1, where theplurality of geometric characteristics of the well tool comprise threedimensional surface data characterizing the shape of outwardly facing,lateral surfaces of the well tool; where the plurality of geometriccharacteristics of the well comprise three dimensional surface datacharacterizing the shape of inwardly facing, lateral surfaces of thewell that will be in or near contact with the well tool during itsoperation; and where determining, with the computing system, based onthe geometric characteristics of the well tool and the well, a predictedreaction force on a portion of the well tool comprises determining, withthe computing system, based on the three dimensional surface data of thewell tool and the well, a predicted reaction force on a portion of thewell tool.
 4. The computer-implemented method of claim 1, where thesurface associated with the well tool comprises a surface of one or moreof a line supporting the well tool in the well.
 5. Thecomputer-implemented method of claim 1, where the well tool comprises ajarring tool for applying an impact force to the device and thepredicted reaction force has a component in a direction opposing theimpact force.
 6. The computer-implemented method of claim 5, where thewell tool further comprises a pulling tool directly connecting thejarring tool to the device.
 7. The computer-implemented method of claim1, further comprising: receiving, with the computing system, inputsrepresenting a plurality of geometric characteristics of a well stringincluding the well tool; and determining, with the computing system,based on the geometric characteristics of the well string, the well tooland the well, a predicted reaction force on a portion of the well tooldue to contact between a surface of the well string and a surface of thewell that affects operation of the well tool in applying force to thedevice.
 8. The computer-implemented method of claim 1, furthercomprising: receiving, with the computing system, inputs representing aplurality of geometric characteristics of a second well tool in the wellstring that operates in applying the force to the device in the well;and determining, with the computing system, based on the geometriccharacteristics of the well string, the well tool, the second well tooland the well, a second predicted reaction force on a portion of thesecond well tool due to contact between a surface associated with thesecond well tool and a surface of the well that affects the operation ofthe second well tool in applying force to the device.
 9. Thecomputer-implemented method of claim 8, further comprising determining,with the computing system, a net predicted total force that can beapplied by the well tool and the second well to the device in the wellbased on the predicted reaction force and second predicted reactionforce.
 10. The computer-implemented method of claim 9, where the welltool comprises a detent jarring tool for applying an impact force to thedevice and the second well tool comprises a spang jarring tool forapplying an impact force to the device.
 11. The computer-implementedmethod of claim 1, where the computing system comprises an adaptedmachine learning model calibrated using historical data of other welltools, other wells and predicted reaction forces associated with theother well tools and other wells; and where determining a predictedreaction force comprises matching, with the adaptive machine learningmodel, the historical data with at least a portion of the inputsrepresenting a plurality of geometric characteristics of the well tooland the inputs representing a plurality of geometric characteristics ofthe well.
 12. The computer-implemented method of claim 11, wheredetermining a predicted reaction force further comprises matching, withthe adaptive machine learning model, the historical data with inputsrepresenting other characteristics of the well tool and well.
 13. Thecomputer-implemented method of claim 1, where the predicted reactionforce is generated as the tool moves in an uphole direction through thewell while applying an uphole oriented force to the device.
 14. Thecomputer-implemented method of claim 1, where the predicted reactionforce is generated as the tool moves axially through the well in atelescoping arrangement with a portion of a downhole tool string.
 15. Anapparatus comprising instructions embodied on a tangible, non-transitorycomputer-readable media, the instructions operable when executed tocause a computing system to perform operations comprising: receiving,with a computing system, inputs representing a plurality of geometriccharacteristics of a well tool, the well tool operable to apply a forceon a downhole well device by moving through the well relative to thedevice; receiving, with the computing system, inputs representing aplurality of geometric characteristics of the well; determining, withthe computing system, based on the geometric characteristics of the welltool and the well, a predicted reaction force on a portion of the welltool that affects an operation of the well tool in applying the force tothe device, the predicted reaction force generated at a contact betweena surface associated with the well tool and a surface of the well as thetool moves through the well while applying the force to the downholedevice; and comparing the predicted reaction force to a failure force ofthe well tool to determine a success of the well tool at passing movingthrough the well while applying the force to the downhole device. 16.The apparatus of claim 15, where the operations further comprisedetermining, with the computing system, a net predicted force that canbe applied by the well tool to the device in the well based on thepredicted reaction force.
 17. The apparatus of claim 15 where theplurality of geometric characteristics of the well tool comprise threedimensional surface data characterizing the shape of outwardly facing,lateral surfaces of the well tool; where the plurality of geometriccharacteristics of the well comprise three dimensional surface datacharacterizing the shape of inwardly facing, lateral surfaces of thewell that will be in or near contact with the well tool during itsoperation; and where determining, with the computing system, based onthe geometric characteristics of the well tool and the well, a predictedreaction force on a portion of the well tool comprises determining, withthe computing system, based on the three dimensional surface data of thewell tool and the well, a predicted reaction force on a portion of thewell tool.
 18. The apparatus of claim 15 where the surface associatedwith the well tool comprises a surface of one or more of a linesupporting the well tool in the well.
 19. The apparatus of claim 15,where the well tool comprises a jarring tool for applying an impactforce to the device and the predicted reaction force has a component ina direction opposing the impact force.
 20. The apparatus of claim 15,where the operations further comprise: receiving, with the computingsystem, inputs representing a plurality of geometric characteristics ofa well string including the well tool; and determining, with thecomputing system, based on the geometric characteristics of the wellstring, the well tool and the well, a predicted reaction force on aportion of the well tool due to contact between a surface of the wellstring and a surface of the well that affects operation of the well toolin applying force to the device.
 21. A computing system, comprising amemory, a processor, and instructions stored in the memory and operablewhen executed by the processor to perform operations comprising:receiving, with the computing system, inputs representing a plurality ofgeometric characteristics of a well tool, the well tool operable toapply a force on a downhole well device by moving through the wellrelative to the device; receiving, with the computing system, inputsrepresenting a plurality of geometric characteristics of the well;determining, with the computing system, based on the geometriccharacteristics of the well tool and the well, a predicted reactionforce on a portion of the well tool that affects an operation of thewell tool in applying the force to the device, the predicted reactionforce generated at a contact between a surface associated with the welltool and a surface of the well as the tool moves through the well whileapplying the force to the downhole device; and comparing the predictedreaction force to a failure force of the well tool to determine asuccess of the well tool at passing moving through the well whileapplying the force to the downhole device.
 22. The computing system ofclaim 21, where the operations further comprise determining, with thecomputing system, a net predicted force that can be applied by the welltool to the device in the well based on the predicted reaction force.23. The computing system of claim 21, where the plurality of geometriccharacteristics of the well tool comprise three dimensional surface datacharacterizing the shape of outwardly facing, lateral surfaces of thewell tool; where the plurality of geometric characteristics of the wellcomprise three dimensional surface data characterizing the shape ofinwardly facing, lateral surfaces of the well that will be in or nearcontact with the well tool during its operation; and where determining,with the computing system, based on the geometric characteristics of thewell tool and the well, a predicted reaction force on a portion of thewell tool comprises determining, with the computing system, based on thethree dimensional surface data of the well tool and the well, apredicted reaction force on a portion of the well tool.
 24. Thecomputing system of claim 21, where the surface associated with the welltool comprises a surface of one or more of a line supporting the welltool in the well.
 25. The computing system of claim 21, where the welltool comprises a jarring tool for applying an impact force to the deviceand the predicted reaction force has a component in a direction opposingthe impact force.
 26. The computing system of claim 21, where theoperations further comprise: receiving, with the computing system,inputs representing a plurality of geometric characteristics of a wellstring including the well tool; and determining, with the computingsystem, based on the geometric characteristics of the well string, thewell tool and the well, a predicted reaction force on a portion of thewell tool due to contact between a surface of the well string and asurface of the well that affects operation of the well tool in applyingforce to the device.
 27. A method, comprising: inputting, into acomputing system, inputs representing a plurality of geometriccharacteristics of a first configuration of a well string, a pluralityof geometric characteristics of a second configuration of a well string,and a plurality of geometric characteristics of a well, each of thefirst and second configurations comprising a well tool operable to applya force on a downhole well device by moving through the well relative tothe device; initiating, by the computing system, a simulation of anoperation based on an adapted machine learning model algorithm, thesimulation comprising determining a prediction of the force the firstconfiguration of the well string is capable of applying to the downholewell device, as the well tool moves through the well, based on theplurality of the geometric characteristics of the first configuration ofthe well string and the geometric characteristics of the well; anddetermining a prediction of the force the second configuration of thewell string is capable of applying to the downhole well device, as thewell tool moves through the well, based on the plurality of thegeometric characteristics of the second configuration of the wellstring, and the geometric characteristics of the well; and identifying ahigher predicted force by comparing the force that the firstconfiguration of the well string is capable of applying to the device tothe force that the second configuration of the well string is capable ofapplying to the device; accumulating two or more components of theconfiguration of the well string indicated by the computing system ascapable of applying the higher predicted force to the well device whilein the well.
 28. The method of claim 27, where the plurality ofgeometric characteristics of the first and second configurations of thewell string comprise three dimensional surface data characterizing theshape of outwardly facing, lateral surfaces of the configurations of thewell strings.
 29. The method of claim 27, where the plurality ofgeometric characteristics of the well comprise three dimensional surfacedata characterizing the shape of inwardly facing, lateral surfaces ofthe well.
 30. The method of claim 27, further comprising determining apredicted reaction force on a portion of the well tool of each of thefirst and second configurations of the well string that affects anoperation of the well tool in applying the force to the downhole device,the predicted reaction force generated at a contact between a surfaceassociated with the well tool and a surface of the well as the toolmoves through the well while applying the force to the downhole devicecomparing the higher predicted reaction force to a failure force of thewell tool to determine a success of the well tool at moving through thewell while applying the force to the downhole device.